Round-trip time prediction using machine learning methods

I. A. Stepanov^ab, R. E. Ponomarenko^b, D. R. Golovash^b, A. Yu. Pokidko^b, A. I. Get'man<sup>cadb

a</sup> Moscow Institute of Physics and Technology (State University), Dolgoprudny
^b Ivannikov Institute for System Programming of the RAS
^c Lomonosov Moscow State University
^d National Research University Higher School of Economics

Abstract: The congestion control algorithms in the TCP protocol use RTT predictions indirectly or directly to determine congestion. The main algorithm for predicting RTT based on a weighted moving average is the Jacobson Algorithm. However, this algorithm may not work quite efficiently if the RTT is subject to a heavy-tailed distribution. In this paper, we propose an RTT prediction method based on supervised learning in both the offline and online cases. The results show improvement in the performance of algorithms based on supervised learning compared to the classical Jacobson algorithm in terms of MAPE, MAE, and MSE metrics. In addition, the high efficiency of online learning in comparison with offline learning in the case of data drift is shown.

Keywords: TCP, RTT prediction, online learning, Adaptive Random Forest regression.

Language: English

DOI: 10.15514/ISPRAS-2025-37(5)-4